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Statements

Subject Item
n2:RIV%2F68407700%3A21110%2F13%3A00206799%21RIV14-MSM-21110___
rdf:type
n11:Vysledek skos:Concept
dcterms:description
To simplify a numerical analysis of complicated real microstructures, a material representative volume element is defined. It is based on a binary (black and white) image, which statistically resembles a corresponding real microstructure. Several statistical descriptors suitable for the microstructure characterization of a random media can be considered. Then, for example a unit cell can be derived from the optimization procedure formulated in terms of selected statistical descriptors. The aim of the work described in this paper is to resolve this issue using multi-objective optimization techniques. The goal is to approximate as closely as possible the true Pareto front as a trade-off of competing objectives. The performance of the multi-objective algorithm is verified by the reconstruction of given artificial images and is compared with results of the single-objective counterparts. To simplify a numerical analysis of complicated real microstructures, a material representative volume element is defined. It is based on a binary (black and white) image, which statistically resembles a corresponding real microstructure. Several statistical descriptors suitable for the microstructure characterization of a random media can be considered. Then, for example a unit cell can be derived from the optimization procedure formulated in terms of selected statistical descriptors. The aim of the work described in this paper is to resolve this issue using multi-objective optimization techniques. The goal is to approximate as closely as possible the true Pareto front as a trade-off of competing objectives. The performance of the multi-objective algorithm is verified by the reconstruction of given artificial images and is compared with results of the single-objective counterparts.
dcterms:title
Multi-Objective Reconstruction of Random Media Multi-Objective Reconstruction of Random Media
skos:prefLabel
Multi-Objective Reconstruction of Random Media Multi-Objective Reconstruction of Random Media
skos:notation
RIV/68407700:21110/13:00206799!RIV14-MSM-21110___
n11:predkladatel
n22:orjk%3A21110
n3:aktivita
n10:S n10:P
n3:aktivity
P(GAP105/11/0411), S
n3:dodaniDat
n19:2014
n3:domaciTvurceVysledku
n7:1476602 n7:9081429 n7:4695046
n3:druhVysledku
n21:D
n3:duvernostUdaju
n15:S
n3:entitaPredkladatele
n13:predkladatel
n3:idSjednocenehoVysledku
90119
n3:idVysledku
RIV/68407700:21110/13:00206799
n3:jazykVysledku
n5:eng
n3:klicovaSlova
multi-objective optimization; genetic algorithm; non-dominated sorting genetic algorithm; two-point probability function; two-point cluster function; image reconstruction
n3:klicoveSlovo
n12:two-point%20cluster%20function n12:two-point%20probability%20function n12:multi-objective%20optimization n12:non-dominated%20sorting%20genetic%20algorithm n12:genetic%20algorithm n12:image%20reconstruction
n3:kontrolniKodProRIV
[88A1ADD485C1]
n3:mistoKonaniAkce
Cagliari
n3:mistoVydani
Stirling
n3:nazevZdroje
Proceedings of the Third International Conference on Soft Computing Technology in Civil, Structural and Environmental Engineering
n3:obor
n17:JD
n3:pocetDomacichTvurcuVysledku
3
n3:pocetTvurcuVysledku
3
n3:projekt
n4:GAP105%2F11%2F0411
n3:rokUplatneniVysledku
n19:2013
n3:tvurceVysledku
Pospíšilová, Adéla Lepš, Matěj Zeman, Jan
n3:typAkce
n9:WRD
n3:zahajeniAkce
2013-09-03+02:00
s:issn
1759-3433
s:numberOfPages
16
n20:hasPublisher
Civil-Comp Press Ltd
n16:isbn
978-1-905088-58-4
n8:organizacniJednotka
21110